{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1301103b0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "from torchvision import datasets\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "torch.manual_seed(12046)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 10000, 10000)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 准备数据\n",
    "dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
    "# 将数据划分成训练集、验证集、测试集\n",
    "train_set, val_set = random_split(dataset, [50000, 10000])\n",
    "test_set = datasets.MNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())\n",
    "len(train_set), len(val_set), len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建数据读取器\n",
    "train_loader = DataLoader(train_set, batch_size=500, shuffle=True)\n",
    "val_loader = DataLoader(val_set, batch_size=500, shuffle=True)\n",
    "test_loader = DataLoader(test_set, batch_size=500, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 28, 28]) torch.Size([1, 3, 28, 28])\n",
      "tensor([7]) torch.Size([1, 4, 4, 4]) torch.Size([1, 4, 4, 4])\n"
     ]
    }
   ],
   "source": [
    "# 卷积层的几个小技巧\n",
    "\n",
    "# 这个卷积操作，输入和输出的形状是一样的\n",
    "conv3 = nn.Conv2d(3, 3, (3, 3), stride=1, padding=1)\n",
    "x = torch.randn(1, 3, 28, 28)\n",
    "print(x.size(), conv3(x).size())\n",
    "\n",
    "# 这两个卷积操作输出的形状是一样的\n",
    "stride = torch.randint(0, 10, (1,))\n",
    "conv1 = nn.Conv2d(3, 4, (3, 3), stride=stride, padding=1)\n",
    "conv2 = nn.Conv2d(3, 4, (1, 1), stride=stride, padding=0)\n",
    "x = torch.randn(1, 3, 28, 28)\n",
    "print(stride, conv1(x).size(), conv2(x).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 28, 28])\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "The size of tensor a (14) must match the size of tensor b (28) at non-singleton dimension 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-4d7bea514a78>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;31m# 报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mResidualBlockBugVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-4d7bea514a78>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;31m## 如果stride != 1 or in_channel != out_channel，\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;31m## 下面的计算会出错，因为out和inputs的形状不一样\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: The size of tensor a (14) must match the size of tensor b (28) at non-singleton dimension 3"
     ]
    }
   ],
   "source": [
    "# 有漏洞的残差连接\n",
    "class ResidualBlockBugVersion(nn.Module):\n",
    "    \n",
    "    def __init__(self, in_channel, out_channel, stride=1):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(\n",
    "            in_channel, out_channel, (3, 3), \n",
    "            stride=stride, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(out_channel)\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            out_channel, out_channel, (3, 3),\n",
    "            stride=1, padding=1, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channel)\n",
    "            \n",
    "    def forward(self, x):\n",
    "        inputs = x\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.bn2(self.conv2(out))\n",
    "        # 残差连接\n",
    "        ## 如果stride != 1或者in_channel != out_channel，\n",
    "        ## 下面的计算会出错，因为out和inputs的形状不一样\n",
    "        out += inputs\n",
    "        out = F.relu(out)\n",
    "        return out\n",
    "    \n",
    "m = ResidualBlockBugVersion(3, 3)\n",
    "print(m(torch.randn(1, 3, 28, 28)).size())\n",
    "\n",
    "# 报错\n",
    "m = ResidualBlockBugVersion(3, 3, 2)\n",
    "print(m(torch.randn(1, 3, 28, 28)).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 4, 14, 14])\n"
     ]
    }
   ],
   "source": [
    "class ResidualBlock(nn.Module):\n",
    "    \n",
    "    def __init__(self, in_channel, out_channel, stride=1):\n",
    "        '''\n",
    "        定义残差块\n",
    "        参数\n",
    "        ----\n",
    "        in_channel ：int，输入通道\n",
    "        out_channel ：int，输出通道\n",
    "        stride ：int，步幅大小\n",
    "        '''\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(\n",
    "            in_channel, out_channel, (3, 3), \n",
    "            stride=stride, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(out_channel)\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            out_channel, out_channel, (3, 3),\n",
    "            stride=1, padding=1, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channel)\n",
    "        self.downsample = None\n",
    "        # 如果stride != 1或者in_channel != out_channel，那么输入的形状和输出的形状不一样\n",
    "        # 使用下面的变换使得两个张量的形状一样\n",
    "        if stride != 1 or in_channel != out_channel:\n",
    "            # 下面两个卷积操作的输出形状是一样的\n",
    "            # Conv2d(in_channel, out_channel, (3, 3), stride, padding=1)\n",
    "            # Conv2d(in_channel, out_channel, (1, 1), stride, padding=0)\n",
    "            self.downsample = nn.Sequential(\n",
    "                nn.Conv2d(in_channel, out_channel, (1, 1), stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(out_channel))\n",
    "            \n",
    "    def forward(self, x):\n",
    "        '''\n",
    "        向前传播\n",
    "        参数\n",
    "        ----\n",
    "        x ：torch.FloatTensor，形状为(B, I, H, W)\n",
    "        '''\n",
    "        inputs = x\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.bn2(self.conv2(out))\n",
    "        # 让输入(inputs)的形状和输出(out)的形状一样\n",
    "        if self.downsample is not None:\n",
    "            inputs = self.downsample(inputs)\n",
    "        out += inputs\n",
    "        out = F.relu(out)\n",
    "        return out\n",
    "\n",
    "m = ResidualBlock(3, 4, 2)\n",
    "print(m(torch.randn(1, 3, 28, 28)).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResNet(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.block1 = ResidualBlock(1, 20)\n",
    "        self.block2 = ResidualBlock(20, 40, stride=2)\n",
    "        self.block3 = ResidualBlock(40, 60, stride=2)\n",
    "        self.block4 = ResidualBlock(60, 80, stride=2)\n",
    "        self.block5 = ResidualBlock(80, 100, stride=2)\n",
    "        self.block6 = ResidualBlock(100, 120, stride=2)\n",
    "        self.lm = nn.Linear(120, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        '''\n",
    "        向前传播\n",
    "        参数\n",
    "        ----\n",
    "        x ：torch.FloatTensor，形状为(B, 1, 28, 28)\n",
    "        '''\n",
    "        x = self.block1(x) # (B,  20, 28, 28)\n",
    "        x = self.block2(x) # (B,  40, 14, 14)\n",
    "        x = self.block3(x) # (B,  60,  7,  7)\n",
    "        x = self.block4(x) # (B,  60,  4,  4)\n",
    "        x = self.block5(x) # (B,  60,  2,  2)\n",
    "        x = self.block6(x) # (B, 120,  1,  1)\n",
    "        out = self.lm(x.view(x.shape[0], -1)) # (B, 10)\n",
    "        return out\n",
    "\n",
    "model = ResNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_iters = 10\n",
    "\n",
    "def estimate_loss(model):\n",
    "    re = {}\n",
    "    # 将模型切换至评估模式\n",
    "    model.eval()\n",
    "    re['train'] = _loss(model, train_loader)\n",
    "    re['val'] = _loss(model, val_loader)\n",
    "    re['test'] = _loss(model, test_loader)\n",
    "    # 将模型切换至训练模式\n",
    "    model.train()\n",
    "    return re\n",
    "\n",
    "@torch.no_grad()\n",
    "def _loss(model, data_loader):\n",
    "    \"\"\"\n",
    "    计算模型在不同数据集下面的评估指标\n",
    "    \"\"\"\n",
    "    loss = []\n",
    "    accuracy = []\n",
    "    data_iter = iter(data_loader)\n",
    "    for k in range(eval_iters):\n",
    "        inputs, labels = next(data_iter)\n",
    "        B = inputs.shape[0]\n",
    "        logits = model(inputs)\n",
    "        # 计算模型损失\n",
    "        loss.append(F.cross_entropy(logits, labels))\n",
    "        # 计算预测的准确率\n",
    "        _, predicted = torch.max(logits, 1)\n",
    "        accuracy.append((predicted == labels).sum() / B)\n",
    "    re = {\n",
    "        'loss': torch.tensor(loss).mean().item(),\n",
    "        'accuracy': torch.tensor(accuracy).mean().item()\n",
    "    }\n",
    "    return re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_resnet(model, optimizer, data_loader, epochs=10, penalty=[]):\n",
    "    lossi = []\n",
    "    for epoch in range(epochs):\n",
    "        for i, data in enumerate(data_loader, 0):\n",
    "            inputs, labels = data\n",
    "            optimizer.zero_grad()\n",
    "            logits = model(inputs)\n",
    "            loss = F.cross_entropy(logits, labels)\n",
    "            lossi.append(loss.item())\n",
    "            # 增加惩罚项\n",
    "            for p in penalty:\n",
    "                loss += p(model)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "        # 评估模型，并输出结果\n",
    "        stats = estimate_loss(model)\n",
    "        train_loss = f'train loss {stats[\"train\"][\"loss\"]:.4f}'\n",
    "        val_loss = f'val loss {stats[\"val\"][\"loss\"]:.4f}'\n",
    "        test_loss = f'test loss {stats[\"test\"][\"loss\"]:.4f}'\n",
    "        print(f'epoch {epoch:>2}: {train_loss}, {val_loss}, {test_loss}')\n",
    "        train_acc = f'train acc {stats[\"train\"][\"accuracy\"]:.4f}'\n",
    "        val_acc = f'val acc {stats[\"val\"][\"accuracy\"]:.4f}'\n",
    "        test_acc = f'test acc {stats[\"test\"][\"accuracy\"]:.4f}'\n",
    "        print(f'{\"\":>10}{train_acc}, {val_acc}, {test_acc}')\n",
    "    return lossi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  0: train loss 0.0820, val loss 0.1000, test loss 0.0955\n",
      "          train acc 0.9728, val acc 0.9646, test acc 0.9686\n",
      "epoch  1: train loss 0.0826, val loss 0.0873, test loss 0.0864\n",
      "          train acc 0.9746, val acc 0.9744, test acc 0.9734\n",
      "epoch  2: train loss 0.0523, val loss 0.0689, test loss 0.0634\n",
      "          train acc 0.9860, val acc 0.9798, test acc 0.9804\n",
      "epoch  3: train loss 0.0781, val loss 0.1083, test loss 0.0830\n",
      "          train acc 0.9762, val acc 0.9700, test acc 0.9754\n",
      "epoch  4: train loss 0.0157, val loss 0.0412, test loss 0.0365\n",
      "          train acc 0.9930, val acc 0.9870, test acc 0.9894\n"
     ]
    }
   ],
   "source": [
    "stats['resnet'] = train_resnet(model, optim.Adam(model.parameters(), lr=0.01), train_loader, epochs=5)"
   ]
  }
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